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Price Discovery, Volatility Spillovers and Adequacy of Speculation in Cheese Spot and Futures Markets Marin Bozic University of Minnesota-Twin Cities NDSU Seminar, 10/28/2011 1

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Motivation: Volatility in Dairy Sector 2

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3 Motivation: How to Model Agricultural Prices

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4 Motivation: How to Model Speculative Influence?

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Volatility in the Dairy Sector: Why? S D D′D′ Quantity Price 5

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Volatility in the Dairy Sector: Why? 6

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Dealing with High Volatility Price Support Programs Milk Income Loss Contract 7 Catastrophic Insurance (LGM-Dairy) Market-based instruments: Dairy Futures & Options, OTCs Herd Termination Programs Social Insurance Supply Management

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Purpose of this paper Where does the new information about prices originate? Are there volatility spillovers between dairy markets? Did speculators contribute to rising volatility in the market? 8

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Pricing Milk in the U.S. : 1. Government 9

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Spot market trades daily for 15 minutes each morning. No cash market for dry whey or milk. 10 Pricing Milk in the U.S. : 2. CME Cash Market

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Thin Slicing 11 -Markets are very thin -USDA reports results of daily trading as well as weekly average -Prices for cheese used as benchmark in setting prices in direct transactions across the nation

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12 Pricing Milk in the U.S. : 3. CME Futures Market

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Class III Milk Futures: Comparing mid-October liquidity

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Functions of the futures market: Price Discovery 14

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Questions of interest How do futures and cash market for cheese interact? – Price discovery – Volatility spillovers Impact of speculation on dairy futures 15

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A typical modeling approach Test if cash and futures are stationary – If yes: VAR – If no: Co-integration Volatility spillovers: – If high-frequency: realized volatility/VAR – If low-frequency: GARCH Effects of speculation – If high-frequency: additional regressor in VAR – If low-frequency: BEKK-X, EGARCH-X 16

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VAR vs. co-integration 17 Case 1: Variables of interest are stationary (no persistent shocks) Instruction: Build a vector autoregressive model Case 2: Variables are non-stationary (some shocks are persistent) Instruction: Build a co-integration model

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Data limitations Cash market is thin – Closing price may indicate unfilled bid/uncovered offer – No cash market for manufacturing grade milk or dry whey Futures market – Cheese futures market did not exist until 07/2010 – Data on speculative positions available only weekly 18

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Implied Cheese Futures 19

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Implied vs. observed cheese futures 20

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21 Creating Nearby Futures Price Series

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Unit root tests of cheese cash and futures time series 1.Augmented Dickey-Fuller (Said and Dickey, 1984) Null: : (unit root present; no drift) 2. Phillips-Perron (1988): Null: alpha=0, rh 1 22

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Unit Root Tests Results: Cash Cheese 23

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Unit Root Tests Results: Cheese Futures 24

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Devil is in the details: accounting for past lagged differenced futures 25

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Unit Root Tests Results: Cheese Futures 26

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Making sense of unit root results: 1. Economic Theory Cash price analysis based on production theory – Perfect competition: zero long-run economic profit for the marginal producer Profit margin will be a mean-reverting time series – If long-run industry average cost curve is flat Permanent shifts in demand temporary shifts to cash prices Permanent changes in input prices structural change If supply is inelastic in short run high persistency of shocks – If long-run AC curve is sloped Permanent shifts in demand permanent shocks to cash price series 27

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Making sense of unit root results: 1. Economic Theory Futures price analysis based on finance theory Efficient market prices in a single contract will be martingales if the marginal risk premium is zero; submartingales (downward biased) if marginal risk premium is positive Supermartingales (upward biased) if marginal risk premium is negative - In any case: efficient futures prices will be non-stationary, i.e. all shocks to futures prices are permanent 28

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Making sense of unit root results: 2. Time Series Modeling Exercise What if there was a market in which cash price was indeed second-order stationary If there was a futures contract designed to cash-settle against such a spot price, what would be the characteristics of that time series? For simplicity, assume no marginal risk premium 29

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Making sense of unit root results: 2. Time Series Modeling Exercise 30

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Making sense of unit root results: 2. Time Series Modeling Exercise - Results 31 1.Martingale Property within each contract 2.Nearby series not a martingale

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Making sense of unit root results: 2. Time Series Modeling Exercise - What would Unit Root Tests Show? 32 Cash Prices: 1) Null would likely be rejected Futures prices: 2) for a single contract, null would likely not be rejected 3) Null more likely to be rejected for n-th than for n+1 nearby series 4) More obs. between rollover periods null less likely to be rejected (reducing data frequency increases likelihood of rejecting the null)

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Unit Root Tests: Conclusions Cash Cheese is mean reverting Nearby cheese futures are nonlinear – Unit-root processes within each contract – Mean-reverting at contract rollover Next: How to model this? 33

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Modeling information flows Causality in mean Second-order causality (causality in variance) 34

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Second order non-causality Granger non-causality: knowing the futures price does not help us predict cash (and vice versa). Second-order non-causality: knowing the futures price history may or may not help you predict the cash price level, but it does not influence the magnitude of cash price forecast conditional variance Non-causality in variance: Granger non-causality and second-order non-causality combined 35

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GARCH-BEKK and second-order non- causality 36

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Adding speculators The key problem is how to preserve positive definiteness of conditional variance matrix Adding another term? Sign of the impact of additional regressor is restricted to be positive but we must have flexibility! 37

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GARCH-MEX 38

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GARCH-MEX 39

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Measuring “Adequacy” of Speculation Based on Working (1960) – “Working’s T” The idea is that when hedgers are net long, long speculative position is not really ‘necessary’. But if it is there, it may “grease up” the market, or may be indicative of excessive speculation if T is too high. So, if 40

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Measuring “Adequacy” of Speculation Likewise, if hedgers are net short, then only long speculative positions are needed to balance the market. Having long speculators may help, but too much of it may be “excessive”. So, if Key assumption: how to treat unreportables. 41

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Results: Information flows in mean 42

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Results: Information flows in mean 43

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Results: Information flows in mean 44

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Results: Information flows in mean 45

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Results: Information flows in mean Conclusion: Using daily close prices at either daily or weekly frequency, using either nominal or log prices, and either control for heteroskedasticity or not – we always find that adjustment to spread between cash and futures is done in the cash market 46

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Results: volatility spillovers 47 In a model where only GARCH-BEKK is added to error-correction model for mean, we find bi- directional volatility spillovers.

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Results: Speculative Influence 48

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Conclusions Not likely that speculators increased volatility in dairy futures; if anything, speculative presence seems to be below what is deemed required for liquid market. GARCH-MEX has a potential for allowing flexible functional form, but restriction on correlation coefficient may flip the sign (and reduce the likelihood) 49

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